# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import re
from typing import Dict, List, Optional, Union

import numpy as np
import sentencepiece

from nemo.collections.common.parts.utils import if_exist
from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
from nemo.utils import logging

__all__ = ['SentencePieceTokenizer', 'create_spt_model']


class SentencePieceTokenizer(TokenizerSpec):
    '''
    Sentencepiecetokenizer https://github.com/google/sentencepiece.
        Args:
        model_path: path to sentence piece tokenizer model. To create the model use create_spt_model()
        special_tokens: either list of special tokens or dictionary of token name to token value
        legacy: when set to True, the previous behavior of the SentecePiece wrapper will be restored, 
            including the possibility to add special tokens inside wrapper.
    '''

    def __init__(
        self, model_path: str, special_tokens: Optional[Union[Dict[str, str], List[str]]] = None, legacy: bool = False
    ):
        if not model_path or not os.path.exists(model_path):
            raise ValueError(f"model_path: {model_path} is invalid")
        self.tokenizer = sentencepiece.SentencePieceProcessor()
        self.tokenizer.Load(model_path)

        self.original_vocab_size = self.tokenizer.get_piece_size()
        self.vocab_size = self.tokenizer.get_piece_size()
        self.legacy = legacy
        self.special_token_to_id = {}
        self.id_to_special_token = {}
        if special_tokens:
            if not self.legacy:
                raise ValueError(
                    "Special tokens must be None when legacy is set to False. Provide special tokens at train time."
                )
            self.add_special_tokens(special_tokens)

    def text_to_tokens(self, text):
        if self.legacy:
            tokens = []
            idx = 0
            last_idx = 0

            while 1:
                indices = {}

                for token in self.special_token_to_id:
                    try:
                        indices[token] = text[idx:].index(token)
                    except ValueError:
                        continue

                if len(indices) == 0:
                    break

                next_token = min(indices, key=indices.get)
                next_idx = idx + indices[next_token]

                tokens.extend(self.tokenizer.encode_as_pieces(text[idx:next_idx]))
                tokens.append(next_token)
                idx = next_idx + len(next_token)

            tokens.extend(self.tokenizer.encode_as_pieces(text[idx:]))
            return tokens

        return self.tokenizer.encode_as_pieces(text)

    def text_to_ids(self, text):
        if self.legacy:
            ids = []
            idx = 0
            last_idx = 0

            while 1:
                indices = {}

                for token in self.special_token_to_id:
                    try:
                        indices[token] = text[idx:].index(token)
                    except ValueError:
                        continue

                if len(indices) == 0:
                    break

                next_token = min(indices, key=indices.get)
                next_idx = idx + indices[next_token]

                ids.extend(self.tokenizer.encode_as_ids(text[idx:next_idx]))
                ids.append(self.special_token_to_id[next_token])
                idx = next_idx + len(next_token)

            ids.extend(self.tokenizer.encode_as_ids(text[idx:]))
            return ids

        return self.tokenizer.encode_as_ids(text)

    def tokens_to_text(self, tokens):
        if isinstance(tokens, np.ndarray):
            tokens = tokens.tolist()

        return self.tokenizer.decode_pieces(tokens)

    def ids_to_text(self, ids):
        if isinstance(ids, np.ndarray):
            ids = ids.tolist()

        if self.legacy:
            text = ""
            last_i = 0

            for i, id in enumerate(ids):
                if id in self.id_to_special_token:
                    text += self.tokenizer.decode_ids(ids[last_i:i]) + " "
                    text += self.id_to_special_token[id] + " "
                    last_i = i + 1

            text += self.tokenizer.decode_ids(ids[last_i:])
            return text.strip()

        return self.tokenizer.decode_ids(ids)

    def token_to_id(self, token):
        if self.legacy and token in self.special_token_to_id:
            return self.special_token_to_id[token]

        return self.tokenizer.piece_to_id(token)

    def ids_to_tokens(self, ids):
        tokens = []
        for id in ids:
            if id >= self.original_vocab_size:
                tokens.append(self.id_to_special_token[id])
            else:
                tokens.append(self.tokenizer.id_to_piece(id))
        return tokens

    def tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
        if isinstance(tokens, str):
            tokens = [tokens]
        ids = []
        for token in tokens:
            ids.append(self.token_to_id(token))
        return ids

    def add_special_tokens(self, special_tokens):
        if not self.legacy:
            raise AttributeError("Special Token addition does not work when legacy is set to False.")

        if isinstance(special_tokens, list):
            for token in special_tokens:
                if (
                    self.tokenizer.piece_to_id(token) == self.tokenizer.unk_id()
                    and token not in self.special_token_to_id
                ):
                    self.special_token_to_id[token] = self.vocab_size
                    self.id_to_special_token[self.vocab_size] = token
                    self.vocab_size += 1
        elif isinstance(special_tokens, dict):
            for token_name, token in special_tokens.items():
                setattr(self, token_name, token)
                if (
                    self.tokenizer.piece_to_id(token) == self.tokenizer.unk_id()
                    and token not in self.special_token_to_id
                ):
                    self.special_token_to_id[token] = self.vocab_size
                    self.id_to_special_token[self.vocab_size] = token
                    self.vocab_size += 1

    @property
    def pad_id(self):
        if self.legacy:
            pad_id = self.tokens_to_ids([self.pad_token])[0]
        else:
            pad_id = self.tokenizer.pad_id()
        return pad_id

    @property
    def bos_id(self):
        if self.legacy:
            bos_id = self.tokens_to_ids([self.bos_token])[0]
        else:
            bos_id = self.tokenizer.bos_id()
        return bos_id

    @property
    def eos_id(self):
        if self.legacy:
            eos_id = self.tokens_to_ids([self.eos_token])[0]
        else:
            eos_id = self.tokenizer.eos_id()
        return eos_id

    @property
    def sep_id(self):
        if self.legacy:
            return self.tokens_to_ids([self.sep_token])[0]
        else:
            raise NameError("Use function token_to_id to retrieve special tokens other than unk, pad, bos, and eos.")

    @property
    def cls_id(self):
        if self.legacy:
            return self.tokens_to_ids([self.cls_token])[0]
        else:
            raise NameError("Use function token_to_id to retrieve special tokens other than unk, pad, bos, and eos.")

    @property
    def mask_id(self):
        if self.legacy:
            return self.tokens_to_ids([self.mask_token])[0]
        else:
            raise NameError("Use function token_to_id to retrieve special tokens other than unk, pad, bos, and eos.")

    @property
    def unk_id(self):
        return self.tokenizer.unk_id()

    @property
    def additional_special_tokens_ids(self):
        """Returns a list of the additional special tokens (excluding bos, eos, pad, unk). Used to return sentinel tokens for e.g. T5."""
        special_tokens = set(
            [self.bos_token, self.eos_token, self.pad_token, self.mask_token, self.cls_token, self.sep_token]
        )
        return [v for k, v in self.special_token_to_id.items() if k not in special_tokens]

    @property
    def vocab(self):
        main_vocab = [self.tokenizer.id_to_piece(id) for id in range(self.tokenizer.get_piece_size())]
        special_tokens = [
            self.id_to_special_token[self.original_vocab_size + i]
            for i in range(self.vocab_size - self.original_vocab_size)
        ]
        return main_vocab + special_tokens


def create_spt_model(
    data_file: str,
    vocab_size: int,
    sample_size: int,
    do_lower_case: bool,
    tokenizer_type: str = 'unigram',
    output_dir: Optional[str] = None,
    character_coverage: float = 1.0,
    train_extremely_large_corpus: bool = False,
    max_sentencepiece_length: int = -1,
    bos: bool = False,
    eos: bool = False,
    pad: bool = False,
    control_symbols: List[str] = None,
    user_defined_symbols: List[str] = None,
):
    """
    Creates sentence piece tokenizer model from data file.
    Args:
        data_file: data file
        vocab_size: vocabulary size
        sample_size: maximum size of sentences the trainer loads
        do_lower_case: if text should be lower cased before tokenizer model is created
        character_coverage: float value between 0 and 1 (as a percentage). For languages with a vast charset,
            can be < 1.0, but for all other languages, it should be set as 1.0
        output_dir: folder to save created tokenizer model. If not specified will store model at data_file/../spt folder
        train_extremely_large_corpus: If training on huge datasets, pass this flag to allow SentencePiece
            to build the tokenizer.
        max_sentencepiece_length: Limits the maximum length of the SentencePiece subword that can be constructed.
            By default, no limit is placed.
        bos: when True, bos token "<s>" is added to the vocabulary.
        eos: when True, eos token "</s>" is added to the vocabulary.
        pad: when True, pad token "<pad>" is added to the vocabulary.
        control_symbols: control symbols to add to tokenizer, as defined by sentencepiece.
            These tokens get removed at decode time and are not encoded from the text - can only be added to the input programatically.
        user_defined_symbols: user symbols to add to tokenizer, as defined by sentencepiece.
            These tokens remain in the decoded text and are encoded automatically when present in the input text.
    """

    if not data_file or not os.path.exists(data_file):
        raise ValueError(f"data_file must be valid file path, but got {data_file}")
    data_dir = os.path.dirname(data_file)
    vocab = []
    special_tokens = ["<s>", "</s>", "<pad>", "<unk>"]
    if not output_dir:
        output_dir = f'{data_dir}/spt'
    if if_exist(output_dir, ['tokenizer.model']):
        logging.info(f"tokenizer model {output_dir}/tokenizer.model already exists")
        return f'{output_dir}/tokenizer.model', f'{output_dir}/vocab.txt'
    logging.info(f'Processing {data_file} and store at {output_dir}')
    os.makedirs(output_dir, exist_ok=True)

    cmd = (
        f"--input={data_file} --model_prefix={output_dir}/tokenizer "
        f"--vocab_size={vocab_size} "
        f"--shuffle_input_sentence=true --hard_vocab_limit=false "
        f"--model_type={tokenizer_type} "
        f"--character_coverage={character_coverage}"
    )

    pad_id = 3
    if not bos:
        pad_id -= 1
        cmd += " --bos_id=-1"

    if not eos:
        pad_id -= 1
        cmd += " --eos_id=-1"

    if pad:
        cmd += f" --pad_id={pad_id}"

    if control_symbols:
        control_string = (",").join(control_symbols)
        cmd += f" --control_symbols={control_string}"
        special_tokens += control_symbols

    if user_defined_symbols:
        user_string = (",").join(user_defined_symbols)
        cmd += f" --user_defined_symbols={user_string}"
        special_tokens += user_defined_symbols

    if do_lower_case:
        cmd += " --normalization_rule_name=nmt_nfkc_cf"

    if sample_size > 0:
        cmd += f" --input_sentence_size={sample_size}"

    if train_extremely_large_corpus:
        cmd += " --train_extremely_large_corpus=true"

    if max_sentencepiece_length >= 0:
        cmd += f" --max_sentencepiece_length={max_sentencepiece_length}"

    sentencepiece.SentencePieceTrainer.Train(cmd)

    # Add BERT control symbols
    tokens = []

    with open(f"{output_dir}/tokenizer.vocab", "r") as f:
        # Read tokens from each line and parse for vocab
        for line in f:
            piece = line.split("\t")[0]
            if piece in special_tokens:
                # skip special tokens
                continue
            token = piece[1:] if piece.startswith("▁") else f"##{piece}"

            if len(token) > 0:
                tokens.append(token)
            else:
                tokens.append(piece[0])

    vocab.extend(tokens)

    # Save vocabulary to output file
    vocab_file = f'{output_dir}/vocab.txt'
    with open(vocab_file, "w") as f:
        for token in vocab:
            f.write(f"{token}\n")
    return f'{output_dir}/tokenizer.model', vocab_file
